| --- | |
| name: plan-exit-review | |
| version: 2.0.0 | |
| description: | | |
| Review a plan thoroughly before implementation. Challenges scope, reviews | |
| architecture/code quality/tests/performance, and walks through issues | |
| interactively with opinionated recommendations. | |
| allowed-tools: | |
| - Read | |
| - Grep |
| to check if the server works - https://webrtc.github.io/samples/src/content/peerconnection/trickle-ice | |
| stun: | |
| stun.l.google.com:19302, | |
| stun1.l.google.com:19302, | |
| stun2.l.google.com:19302, | |
| stun3.l.google.com:19302, | |
| stun4.l.google.com:19302, | |
| stun.ekiga.net, | |
| stun.ideasip.com, |
Caution
As of August 26th 2026, Discord has started suspending quest access of people caught automating quest completion.
Some users have received the following system message:
There isn't much I can do to make the script undetected, so use it at your own risk, as you WILL get flagged by doing so.
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
Operations Security (OpSec) is about controlling the information you leak into the world. In an age of data brokers, constant surveillance, and automated tracking, practicing good OpSec is necessary.
Before adopting any of these practices, you must first define your Threat Model.
Spectrum from Eric Murphy's YouTube video.
To verify a shasum or any other integrity hash, from a website that hosts a file with its shasum, you would normally take the following approach:
sha256sum -c SHASUMSIf the file integrity is intact then it would output:
filename: OK
But what if you only have the file and the shasum and no file? Case in point: Downloading boost libraries from official website
| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
A hands-on one semester course where students build their own compiler from scratch, starting from elementwise programs and ending with training SOTA LLMs on GPUs. This course aggressively builds on the previous week, and is an exercise in slop management. If you let any slop in early, it will compound and you will not finish the class.
Course description: This course covers the design and implementation of a modern machine learning compiler, and examines the interaction between IR design, hardware capabilities, and the structure of machine learning programs. Topics covered include term rewriting, code generation, movement operators, kernel fusion, memory hierarchies, GPU architecture, automatic differentiation, and flash attention. It is a project course, providing experience with performance-oriented programming, managing a codebase that grows all semester, and working in 1 or 2 person teams, culminating in a compiler capable of training modern LLMs.
Prerequisites: This c